#----------------------------------------------
# -*- encoding=utf-8 -*-                      #
# __author__:'xiaojie'                        #
# CreateTime:                                 #
#       2019/7/3 22:08                       #
#                                             #
#               天下风云出我辈，                 #
#               一入江湖岁月催。                 #
#               皇图霸业谈笑中，                 #
#               不胜人生一场醉。                 #
#----------------------------------------------
# 耦合生成对抗网络
# https://github.com/eriklindernoren/Keras-GAN/blob/master/cogan/cogan.py
# 生成器的共享权重应该在前几层，而判别器的共享权重应该在后几层。

import scipy

from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils import plot_model

import matplotlib.pyplot as plt

import sys

import numpy as np

class COGAN():
    """Reference: https://wiseodd.github.io/techblog/2017/02/18/coupled_gan/"""
    def __init__(self):
        self.img_rows = 28
        self.img_cols = 28
        self.channels = 1
        self.img_shape = (self.img_rows,self.img_cols,self.channels)
        self.latent_dim = 100

        optimizer = Adam(0.0002,0.5)

        # Build and compile the discriminator
        self.d1,self.d2 = self.build_discriminators()
        self.d1.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
        self.d2.compile(loss='binary_crossentropy',
                        optimizer=optimizer,
                        metrics=['accuracy'])

        #Build the generator
        self.g1,self.g2 = self.build_generators()

        # The generator takes noise as input and generator imgs
        z = Input(shape=(self.latent_dim,))
        img1 = self.g1(z)
        img2 = self.g2(z)

        # For the combined model we will only train the generators
        self.d1.trainable = False
        self.d2.trainable = False

        # The valid takes generated imgaes as input and determines validity
        valid1 = self.d1(img1)
        valid2 = self.d2(img2)

        # The combined model  (stacked generators and discriminators)
        self.combined = Model(z,[valid1,valid2])
        self.combined.compile(loss=['binary_crossentropy','binary_crossentropy'],
                              optimizer=optimizer)
        plot_model(self.combined,show_shapes=True,to_file='png/combined.png')

    def build_generators(self):

        # Shared weights between generators
        model = Sequential(name='g-shared-weights')
        model.add(Dense(256,input_dim=self.latent_dim))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(BatchNormalization(momentum=0.8))
        plot_model(model,show_shapes=True,to_file='png/g-shared-weights.png')

        noise = Input(shape=(self.latent_dim,))
        feature_repr = model(noise)

        # Generator 1
        g1 = Dense(1024)(feature_repr)
        g1 = LeakyReLU(alpha=0.2)(g1)
        g1 = BatchNormalization(momentum=0.8)(g1)
        g1 = Dense(np.prod(self.img_shape),activation='tanh')(g1)
        img1 = Reshape(self.img_shape)(g1)

        # Generator 2
        g2 = Dense(1024)(feature_repr)
        g2 = LeakyReLU(alpha=0.2)(g2)
        g2 = BatchNormalization(momentum=0.8)(g2)
        g2 = Dense(np.prod(self.img_shape),activation='tanh')(g2)
        img2 = Reshape(self.img_shape)(g2)

        g1 = Model(noise,img1)
        g2 = Model(noise,img2)
        plot_model(g1, show_shapes=True, to_file='png/g1.png')
        plot_model(g2, show_shapes=True, to_file='png/g2.png')
        return g1,g2

    def build_discriminators(self):

        img1 = Input(shape=self.img_shape)
        img2 = Input(shape=self.img_shape)

        # Shared discriminator layers
        model = Sequential(name='d-shared-weights')
        model.add(Flatten(input_shape=self.img_shape))
        model.add(Dense(512))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(256))
        model.add(LeakyReLU(alpha=0.2))

        plot_model(model,show_shapes=True,to_file='png/d-shared-weights.png')

        img1_embedding = model(img1)
        img2_embedding = model(img2)

        # Discriminator 1
        validity1 = Dense(1,activation='sigmoid')(img1_embedding)
        # Discriminator 2
        validity2 = Dense(1,activation='sigmoid')(img2_embedding)

        d1 = Model(img1,validity1,name='d1')
        d2 = Model(img2,validity2,name='d2')
        plot_model(d1,show_shapes=True,to_file='png/d1.png')
        plot_model(d2,show_shapes=True,to_file='png/d2.png')
        return d1,d2

    def train(self,epochs,batch_size=128,sample_interval=50):

        # Load the dataset
        (X_train,_),(_,_) = mnist.load_data()

        # Rescale -1 to 1
        X_train = (X_train.astype(np.float32)-127.5)/127.5
        X_train = np.expand_dims(X_train,axis=3)

        # Images in domain A and B (rotated)
        X1 = X_train[:int(X_train.shape[0]/2)]
        X2 = X_train[int(X_train.shape[0]/2):]
        X2 = scipy.ndimage.interpolation.rotate(X2,90,axes=(1,2))

        # Adversarial ground truths
        valid = np.ones((batch_size,1))
        fake = np.zeros((batch_size,1))

        for epoch in range(epochs):

            # ----------------------
            #  Train Discriminators
            # ----------------------

            # Select a random batch of images
            idx = np.random.randint(0,X1.shape[0],batch_size)
            imgs1= X1[idx]
            imgs2 = X2[idx]

            # Sample noise as generator input
            noise = np.random.normal(0,1,(batch_size,100))

            # Generate a batch of new images
            gen_imgs1 = self.g1.predict(noise)
            gen_imgs2 = self.g2.predict(noise)

            # Train the discriminators
            d1_loss_real = self.d1.train_on_batch(imgs1,valid)
            d2_loss_real = self.d2.train_on_batch(imgs2,valid)
            d1_loss_fake = self.d1.train_on_batch(gen_imgs1,fake)
            d2_loss_fake = self.d2.train_on_batch(gen_imgs2,fake)
            d1_loss = 0.5*np.add(d1_loss_real,d1_loss_fake)
            d2_loss = 0.5*np.add(d2_loss_real,d2_loss_fake)

            # ------------------
            #  Train Generators
            # ------------------

            g_loss = self.combined.train_on_batch(noise,[valid,valid])

            # Plot the progress
            print('%d [D1 loss:%f,acc.: %.2f%%] [D2 loss: %f,acc.:%.2f%%] [G loss:%f]:'\
                  %(epoch,d1_loss[0],100*d1_loss[1],d2_loss[0],100*d2_loss[1],g_loss[0]))

            # If at save interval => save generated image samples
            if epoch % sample_interval ==0:
                self.sample_images(epoch)


    def sample_images(self,epoch):
        r,c = 4,4
        noise = np.random.normal(0,1,(r*int(c/2),100))
        gen_imgs1 = self.g1.predict(noise)
        gen_imgs2 = self.g2.predict(noise)

        gen_imgs = np.concatenate([gen_imgs1,gen_imgs2])

        # Rescale images 0-1
        gen_imgs = 0.5*gen_imgs+0.5
        fig,axs = plt.subplots(r,c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i,j].imshow(gen_imgs[cnt,:,:,0],cmap='gray')
                axs[i,j].axis('off')
                cnt+=1

        fig.savefig('images/mnist_%d.png'%epoch)
        plt.close()


if __name__ == '__main__':
    gan = COGAN()
    gan.train(epochs=30000, batch_size=32, sample_interval=200)







